U.S. patent application number 10/306468 was filed with the patent office on 2004-05-27 for method and apparatus for soft-tissue volume visualization.
Invention is credited to Avinash, Gopal B., Sabol, John Michael, Walker, Matthew Joseph.
Application Number | 20040101104 10/306468 |
Document ID | / |
Family ID | 32325698 |
Filed Date | 2004-05-27 |
United States Patent
Application |
20040101104 |
Kind Code |
A1 |
Avinash, Gopal B. ; et
al. |
May 27, 2004 |
Method and apparatus for soft-tissue volume visualization
Abstract
A method for obtaining data including scanning an object using a
multi-energy computed tomography (MECT) system to obtain data to
generate an anatomical image, and decomposing the obtained data to
generate a first density image representative of bone material and
a second density image representative of soft-tissue. The method
further includes segmenting at least one of the first density image
and the second density image, and volume rendering the second
density image.
Inventors: |
Avinash, Gopal B.; (New
Berlin, WI) ; Sabol, John Michael; (Sussex, WI)
; Walker, Matthew Joseph; (New Berlin, WI) |
Correspondence
Address: |
John S. Beulick
Armstrong Teasdale LLP
Suite 2600
One Metropolitan Sq.
St. Louis
MO
63102
US
|
Family ID: |
32325698 |
Appl. No.: |
10/306468 |
Filed: |
November 27, 2002 |
Current U.S.
Class: |
378/98.12 |
Current CPC
Class: |
A61B 6/12 20130101; A61B
6/463 20130101; A61B 6/405 20130101; A61B 6/466 20130101; A61B
6/4035 20130101; A61B 6/032 20130101; A61B 6/4241 20130101; A61B
6/482 20130101; Y10S 378/901 20130101 |
Class at
Publication: |
378/098.12 |
International
Class: |
H05G 001/64 |
Claims
What is claimed is:
1. A method for obtaining data, said method comprising: scanning an
object using a multi-energy computed tomography (MECT) system to
obtain data to generate an anatomical image; decomposing the
obtained data to generate a first density image representative of
bone material and a second density image representative of
soft-tissue; segmenting at least one of the first density image and
the second density image; and volume rendering the second density
image.
2. A method in accordance with claim 1 wherein segmenting at least
one of the first density image and the second density image
comprises: identifying within the first density image areas smaller
than a predetermined size; and importing data into the second
density image from the anatomical image according to the identified
areas of the first density image.
3. A method in accordance with claim 1 wherein scanning an object
using an MECT comprises scanning the object with a high-energy
projection to obtain a high-energy anatomical image and scanning
the object with a low-energy projection to obtain a low-energy
anatomical image, said decomposing the obtained data comprises:
using the equation 6 I b = H L wb to generate the first density
image, wherein 0<w.sub.s<w.sub.b<1, 1.sub.b is the first
density image, H is the high-energy anatomical image, and L is the
low-energy anatomical image; and using the equation 7 I s = H L w s
to generate the second density image wherein
0<w.sub.s<w.sub.b<1, I.sub.s is the second density image,
H is the high-energy anatomical image, and L is the low-energy
anatomical image.
4. A method in accordance with claim 2 wherein identifying within
the first density image areas smaller than a predetermined size
comprises: thresholding the first density image to produce a first
binary mask image representing bone and calcification; and
extracting areas identified as smaller than the predetermined size
from the first binary mask image to produce a second binary mask
image substantially representing calcification.
5. A method in accordance with claim 1 wherein scanning an object
using an MECT comprises scanning the object with a high-energy
projection to obtain a high-energy anatomical image and scanning
the object with a low-energy projection to obtain a low-energy
anatomical image, said method further comprising contrast matching
the second density image with the high-energy image to produce a
contrast matched soft-tissue image.
6. A method in accordance with claim 5 wherein contrast matching
the second density image with the high-energy image comprises using
the equation 8 I HS = I s w b w b - w s LPF ( I b - w s w b - w s )
,wherein 0<w.sub.s<w.sub.b<1 I.sub.HS is the contrast
matched image, I.sub.b is the first density image, I.sub.s is the
second density image, and LPF is a low pass filter, to match a
contrast of structures within the second density image with
corresponding structures within the high-energy image.
7. A method in accordance with claim 5 wherein importing data into
the second density image from the anatomical image according to the
identified areas of the first density image comprises merging data
regarding the identified areas from the high-energy image with the
contrast matched soft-tissue image to produce a soft-tissue image
including calcification.
8. A method in accordance with claim 1 further comprising
displaying the anatomical image, the first density image, and the
second density image on a display to facilitate surgical instrument
mapping.
9. A multi-energy computed tomography (MECT) system comprising: at
least one radiation source; at least one radiation detector; and a
computer operationally coupled to said radiation source and said
radiation detector, said computer configured to: receive data
regarding a first energy spectrum of a scan of an object; receive
data regarding a second energy spectrum of the scan of the object;
decompose said received data to generate a first density image
representative of bone material and a second density image
representative of soft-tissue; identify within said first density
image areas smaller than a predetermined size; and import data into
said second density image from said data regarding the first energy
spectrum according to said identified areas of said first density
image.
10. An MECT system in accordance with claim 9 wherein said computer
configured to decompose said received data by performing at least
one of a CT number difference decomposition, a Compton and
photoelectric decomposition, a basis material decomposition (BMD),
and a logarithm subtraction decomposition (LSD).
11. An MECT system in accordance with claim 9 wherein said computer
configured to identify within said first density image areas
smaller than a predetermined size by separating out high-contrast
bone regions and high-contrast calcification regions from
low-contrast regions.
12. An MECT system in accordance with claim 9 wherein said computer
further configured to match a contrast of structures within said
second density image with corresponding structures within said data
regarding the first energy spectrum.
13. An MECT system in accordance with claim 9 wherein said computer
further configured to: build a three-dimensional image using said
second density image including said imported data regarding said
identified areas from said data regarding the first energy
spectrum; and rendering said three-dimensional image to produce a
high-contrast rendered image.
14. A multi-energy computed tomography (MECT) system comprising: at
least one radiation source; at least one radiation detector; and a
computer operationally coupled to said radiation source and said
radiation detector, said computer configured to: receive image data
for an object; decompose said received image data into a first
density image representative of bone material and a second density
image representative of soft-tissue; identify within said first
density image areas smaller than a predetermined size; and extract
said identified areas within said first density image using an
algorithm configured to use the connectivity of binary pixels.
15. An MECT system in accordance with claim 14 wherein said
computer further configured to: threshold said first density image
to produce a first binary mask image representing bone and
calcification; and extract said identified areas within said first
density image from said first binary mask to produce a second
binary mask image substantially representing calcification.
16. An MECT system in accordance with claim 15 wherein said
computer configured to import data into said second density image
from said received image data according to said identified areas in
said first density image.
17. An MECT system in accordance with claim 14 wherein said
computer further configured to contrast match said second density
image with said received image data to produce a contrast-matched
soft-tissue image.
18. An MECT system in accordance with claim 17 wherein said
computer further configured to: import data into said
contrast-matched soft-tissue image from said received image data
according to said identified areas of said first density image;
build a three-dimensional image using said contrast-matched
soft-tissue image including said imported data; and render said
three-dimensional image using at least one of volume and surface
rendering to produce a high-contrast rendered image.
19. An MECT system in accordance with claim 14 wherein said
computer configured to display said received image data, said first
density image, and said second density image on a display to
facilitate radiation therapy planning and simulation
calculations.
20. A computer readable medium embedded with a program configured
to instruct a computer to: receive data regarding a first energy
spectrum of a scan of an object; receive data regarding a second
energy spectrum of the scan of the object; decompose said received
data to generate a first density image representative of bone
material and a second density image representative of soft-tissue;
threshold said first density image to produce a first binary mask
image representing bone and calcification; extract areas identified
as smaller than a predetermined size from said first binary mask
image to produce a second binary mask image substantially
representing calcification; and import data into said second
density image from said received data according to said extracted
areas of said first binary mask image.
21. A computer readable medium in accordance with claim 20 wherein
receiving data regarding a first energy spectrum of a scan of an
object comprises receiving a high-energy anatomical image and
receiving data regarding a second energy spectrum of the scan of
the object comprises receiving a low-energy anatomical image, said
computer readable medium configured to construct said computer to
decompose said received data using the equation 9 I b = H L wb to
generate said first density image, wherein
0<w.sub.s<w.sub.b<1, I.sub.b is the first density image, H
is the high-energy anatomical image, and L is the low-energy
anatomical image, and using the equation 10 I s = H L w s ,to
generate said second density image, wherein
0<w.sub.s<w.sub.b<1, I.sub.s is the second density image,
H is the high-energy anatomical image, and L is the low-energy
anatomical image.
22. A computer readable medium in accordance with claim 20 wherein
said computer further configured to instruct said computer to
contrast match said second density image with said received data
regarding the first energy spectrum to produce a contrast-matched
soft-tissue image.
23. A computer readable medium in accordance with claim 22 wherein
said computer readable medium configured to construct said computer
to contrast match said second density image with said data
regarding the first energy spectrum using the equation 11 I HS = I
s w b w b - w s LPF ( I b - w s w b - w s ) ,wherein
0<w.sub.s<w.sub.b<11, I.sub.HS is the contrast matched
image, I.sub.b is the first density image, I.sub.s is the second
density image, and LPF is a low pass boxcar filter.
24. A method for obtaining data, said method comprising: scanning
an object using a multi-energy computed tomography (MECT) system to
obtain data to generate an anatomical image; decomposing the
obtained data to generate a first density image and a second
density image; and volume rendering at least one of the first and
second density image.
25. A method in accordance with claim 24 further comprising at
least one of storing at least one of the volume rendered first and
second density images using a storage device and displaying at
least one of the volume rendered first and second density images on
a display.
Description
BACKGROUND OF THE INVENTION
[0001] This invention relates generally to medical imaging systems,
and more specifically to a method and apparatus for soft-tissue
volume visualization using a medical imaging system.
[0002] In spite of recent advancements in computed tomography (CT)
technology, such as faster scanning speed, larger coverage with
multiple detector rows, and thinner slices, energy resolution is
still a missing piece, namely, a wide x-ray photon energy spectrum
from the x-ray source and a lack of energy resolution from CT
detection systems preclude energy discrimination CT.
[0003] X-ray attenuation through a given object is not a constant.
Rather, x-ray attenuation is strongly dependent on the x-ray photon
energy. This physical phenomenon manifests itself in an image as a
beam-hardening artifact, such as non-uniformity, shading, and
streaks. Some beam-hardening artifacts can be easily corrected, but
others may be more difficult to correct. In general, known methods
to correct beam hardening artifacts include water calibration,
which includes calibrating each CT machine to remove beam hardening
from materials similar to water, and iterative bone correction,
wherein bones are separated in the first-pass image then correcting
for beam hardening from bones in the second-pass. However, beam
hardening from materials other than water and bone, such as metals
and contrast agents, may be difficult to correct. In addition, even
with the above described correction methods, conventional CT does
not provide quantitative image values. Rather, the same material at
different locations often shows different CT numbers.
[0004] Another drawback of conventional CT is a lack of material
characterization. For example, a highly attenuating material with a
low density can result in the same CT number in the image as a less
attenuating material with a high density. Thus, there is little or
no information about the material composition of a scanned object
based solely on the CT number.
[0005] Additionally, similar to traditional x-ray methods, at least
some known soft-tissue volume visualization methods project rays
through an object. However, without segmenting out bone from other
material within the object, visualization of subtle, yet possibly
diagnostically important, structures may be difficult.
Traditionally, bone segmentation of CT images is based on image
characteristics and Hounsfield numbers. Dual-energy decomposition
lends itself nicely for the soft-tissue and bone separation.
However, the methods and systems described below can also remove
calcification, which contains diagnostic information in CT.
BRIEF DESCRIPTION OF THE INVENTION
[0006] In one aspect, a method for obtaining data is provided. The
method includes scanning an object using a multi-energy computed
tomography (MECT) system to obtain data to generate an anatomical
image, and decomposing the obtained data to generate a first
density image representative of bone material and a second density
image representative of soft-tissue. The method further includes
segmenting at least one of the first density image and the second
density image, and volume rendering the second density image.
[0007] In another aspect, a multi-energy computed tomography (MECT)
system is provided. The MECT includes at least one radiation
source, at least one radiation detector, and a computer
operationally coupled to the radiation source and the radiation
detector. The computer is configured to receive data regarding a
first energy spectrum of a scan of an object, receive data
regarding a second energy spectrum of the scan of the object,
decompose the received data to generate a first density image
representative of bone material and a second density image
representative of soft-tissue, identify within the first density
image areas smaller than a predetermined size, and import data into
the second density image from the data regarding the first energy
spectrum according to the identified areas of the first density
image.
[0008] In a further aspect, a multi-energy computed tomography
(MECT) system is provided. The CT system includes at least one
radiation source, at least one radiation detector, and a computer
operationally coupled to the radiation source and the radiation
detector. The computer is configured to receive image data for an
object, decompose the received image data into a first density
image representative of bone material and a second density image
representative of soft-tissue, identify within the first density
image areas smaller than a predetermined size, and extract the
identified areas within the first density image using an algorithm
configured to use the connectivity of binary pixels.
[0009] In an additional aspect, a computer readable medium embedded
with a program is provided. The computer readable medium is
configured to instruct a computer to receive data regarding a first
energy spectrum of a scan of an object, receive data regarding a
second energy spectrum of the scan of the object, decompose the
received data to generate a first density image representative of
bone material and a second density image representative of
soft-tissue, threshold the first density image to produce a first
binary mask image representing bone and calcification, extract
areas identified as smaller than a predetermined size from the
first binary mask image to produce a second binary mask image
substantially representing calcification, and import data into the
second density image from the received data according to the
extracted areas of the first binary mask image.
[0010] In yet another aspect, a method is provided for obtaining
data. The method includes scanning an object using a multi-energy
computed tomography (MECT) system to obtain data to generate an
anatomical image, decomposing the obtained data to generate a first
density image and a second density image, and volume rendering at
least one of the first and second density image.
BRIEF DESCRIPTION OF THE DRAWINGS
[0011] FIG. 1 is a pictorial view of a MECT imaging system.
[0012] FIG. 2 is a block schematic diagram of the system
illustrated in FIG. 1.
[0013] FIG. 3 is a flow chart representing a pre-reconstruction
analysis.
[0014] FIG. 4 is a flow chart representing a post-reconstruction
analysis.
[0015] FIG. 5 is a schematic illustration of a method for volume
visualization using the MECT imaging system illustrated in FIGS. 1
and 2.
[0016] FIG. 6 is a schematic illustration of a method for
soft-tissue volume visualization using the MECT imaging system
illustrated in FIGS. 1 and 2.
[0017] FIG. 7 is a schematic illustration of a known surgical
navigation system.
[0018] FIG. 8 is a schematic illustration of a surgical navigation
system for use with the method described in FIG. 5.
[0019] FIG. 9 is a schematic illustration of a known radiation
therapy system.
[0020] FIG. 10 is a schematic illustration of a radiation therapy
system for use with the method described in FIG. 5.
DETAILED DESCRIPTION OF THE INVENTION
[0021] The methods and apparatus described herein facilitate
augmenting segmentation capabilities of multi-energy imaging with a
method for image-based segmentation. The methods and systems
described herein facilitate real-time volume buildup and
visualization of soft-tissue. More specifically, the methods and
systems described herein facilitate segmenting bone material from
an image while retaining calcification within the image, and
facilitate augmenting segmentation capabilities of multi-energy
imaging to guide surgical navigation and radiation therapy.
[0022] In some known CT imaging system configurations, an x-ray
source projects a fan-shaped beam which is collimated to lie within
an x-y plane of a Cartesian coordinate system and generally
referred to as an "imaging plane". The x-ray beam passes through an
object being imaged, such as a patient. The beam, after being
attenuated by the object, impinges upon an array of radiation
detectors. The intensity of the attenuated radiation beam received
at the detector array is dependent upon the attenuation of an x-ray
beam by the object. Each detector element of the array produces a
separate electrical signal that is a measurement of the beam
intensity at the detector location. The intensity measurements from
all the detectors are acquired separately to produce a transmission
profile.
[0023] In third generation CT systems, the x-ray source and the
detector array are rotated with a gantry within the imaging plane
and around the object to be imaged such that the angle at which the
x-ray beam intersects the object constantly changes. A group of
x-ray attenuation measurements, i.e., projection data, from the
detector array at one gantry angle is referred to as a "view". A
"scan" of the object comprises a set of views made at different
gantry angles, or view angles, during one revolution of the x-ray
source and detector.
[0024] In an axial scan, the projection data is processed to
construct an image that corresponds to a two-dimensional slice
taken through the object. One method for reconstructing an image
from a set of projection data is referred to in the art as the
filtered backprojection technique. This process converts the
attenuation measurements from a scan into integers called "CT
numbers" or "Hounsfield units" (HU), which are used to control the
brightness of a corresponding pixel on a cathode ray tube
display.
[0025] To reduce the total scan time, a "helical" scan may be
performed, wherein the patient is moved while the data for the
prescribed number of slices is acquired. Such a system generates a
single helix from a fan beam helical scan. The helix mapped out by
the fan beam yields projection data from which images in each
prescribed slice may be reconstructed.
[0026] Reconstruction algorithms for helical scanning typically use
helical weighing algorithms that weight the collected data as a
function of view angle and detector channel index. Specifically,
prior to a filtered backprojection process, the data is weighted
according to a helical weighing factor, which is a function of both
the gantry angle and detector angle. The weighted data is then
processed to generate CT numbers and to construct an image that
corresponds to a two-dimensional slice taken through the
object.
[0027] To further reduce the total acquisition time, multi-slice CT
has been introduced. In multi-slice CT, multiple rows of projection
data are acquired simultaneously at any time instant. When combined
with helical scan mode, the system generates a single helix of cone
beam projection data. Similar to the single slice helical,
weighting scheme, a method can be derived to multiply the weight
with the projection data prior to the filtered backprojection
algorithm.
[0028] As used herein, an element or step recited in the singular
and proceeded with the word "a" or "an" should be understood as not
excluding plural said elements or steps, unless such exclusion is
explicitly recited. Furthermore, references to "one embodiment" of
the methods and systems described herein are not intended to be
interpreted as excluding the existence of additional embodiments
that also incorporate the recited features.
[0029] Also as used herein, the phrase "reconstructing an image" is
not intended to exclude embodiments of the methods and systems
described herein in which data representing an image is generated
but a viewable image is not. However, many embodiments generate (or
are configured to generate) at least one viewable image.
[0030] Herein are described methods and apparatus for tissue
characterization and soft-tissue volume visualization using an
energy-discriminating (also known as multi-energy) computed
tomography (MECT) system. First described is MECT system 10 and
followed by applications using MECT system 10.
[0031] Referring to FIGS. 1 and 2, a multi-energy scanning imaging
system, for example, a multi-energy multi-slice computed tomography
(MECT) imaging system 10, is shown as including a gantry 12
representative of a "third generation" CT imaging system. Gantry 12
has an x-ray source 14 that projects a beam of x-rays 16 toward a
detector array 18 on the opposite side of gantry 12. Detector array
18 is formed by a plurality of detector rows (not shown) including
a plurality of detector elements 20 which together sense the
projected x-rays that pass through an object, such as a medical
patient 22. Each detector element 20 produces an electrical signal
that represents the intensity of an impinging x-ray beam and hence
can be used to estimate the attenuation of the beam as it passes
through object or patient 22. During a scan to acquire x-ray
projection data, gantry 12 and the components mounted therein
rotate about a center of rotation 24. FIG. 2 shows only a single
row of detector elements 20 (i.e., a detector row). However,
multi-slice detector array 18 includes a plurality of parallel
detector rows of detector elements 20 such that projection data
corresponding to a plurality of quasi-parallel or parallel slices
can be acquired simultaneously during a scan.
[0032] Rotation of components on gantry 12 and the operation of
x-ray source 14 are governed by a control mechanism 26 of MECT
system 10. Control mechanism 26 includes an x-ray controller 28
that provides power and timing signals to x-ray source 14 and a
gantry motor controller 30 that controls the rotational speed and
position of components on gantry 12. A data acquisition system
(DAS) 32 in control mechanism 26 samples analog data from detector
elements 20 and converts the data to digital signals for subsequent
processing. An image reconstructor 34 receives sampled and
digitized x-ray data from DAS 32 and performs high-speed image
reconstruction. The reconstructed image is applied as an input to a
computer 36, which stores the image in a storage device 38. Image
reconstructor 34 can be specialized hardware or computer programs
executing on computer 36.
[0033] Computer 36 also receives commands and scanning parameters
from an operator via console 40 that has a keyboard. An associated
cathode ray tube display 42 allows the operator to observe the
reconstructed image and other data from computer 36. The operator
supplied commands and parameters are used by computer 36 to provide
control signals and information to DAS 32, x-ray controller 28, and
gantry motor controller 30. In addition, computer 36 operates a
table motor controller 44, which controls a motorized table 46 to
position patient 22 in gantry 12. Particularly, table 46 moves
portions of patient 22 through gantry opening 48.
[0034] In one embodiment, computer 36 includes a device 50, for
example, a floppy disk drive, CD-ROM drive, DVD drive, magnetic
optical disk (MOD) device, or any other digital device including a
network connecting device such as an Ethernet device for reading
instructions and/or data from a computer-readable medium 52, such
as a floppy disk, a CD-ROM, a DVD or an other digital source such
as a network or the Internet, as well as yet to be developed
digital devices. In another embodiment, computer 36 executes
instructions stored in firmware (not shown). Computer 36 is
programmed to perform functions described herein, and as used
herein, the term computer is not limited to just those integrated
circuits referred to in the art as computers, but broadly refers to
computers, processors, microcontrollers, microcomputers,
programmable logic controllers, application specific integrated
circuits, and other programmable circuits, and these terms are used
interchangeably herein. CT imaging system 10 is an
energy-discriminating (also known as multi-energy) computed
tomography (MECT) system in that system 10 is configured to be
responsive to different x-ray spectra. This can be accomplished
with a conventional third generation CT system to acquire
projections sequentially at different x-ray tube potentials. For
example, two scans are acquired either back to back or interleaved
in which the tube operates at 80 kVp and 160 kVp potentials, for
example. Alternatively, special filters are placed between the
x-ray source and the detector such that different detector rows
collect projections of different x-ray energy spectrum.
Alternatively, the special filters that shape the x-ray spectrum
can be used for two scans that are acquired either back to back or
interleaved. Yet another embodiment is to use energy sensitive
detectors such that each x-ray photon reaching the detector is
recorded with its photon energy. Although the specific embodiment
mentioned above refers to a third generation CT system, the methods
described herein equally apply to fourth generation CT systems
(stationary detector--rotating x-ray source) and fifth generation
CT systems (stationary detector and x-ray source).
[0035] There are different methods to obtain multi-energy
measurements: (1) scan with two distinctive energy spectra, (2)
detect photon energy according to energy deposition in the
detector, and (3) photon counting. Photon counting provides clean
spectra separation and an adjustable energy separation point for
balancing photon statistics.
[0036] MECT facilitates reducing or eliminating a plurality of
problems associated with conventional CT, such as, but not limited
to, a lack of energy discrimination and material characterization.
In the absence of object scatter, one only need system 10 to
separately detect two regions of photon energy spectrum, the
low-energy and the high-energy portions of the incident x-ray
spectrum. The behavior at any other energy can be derived based on
the signal from the two energy regions. This phenomenon is driven
by the fundamental fact that in the energy region where medical CT
is interested, two physical processes dominate the x-ray
attenuation, (1) Compton scatter and the (2) photoelectric effect.
Thus, detected signals from two energy regions provide sufficient
information to resolve the energy dependence of the material being
imaged. Furthermore, detected signals from two energy regions
provide sufficient information to determine the relative
composition of an object composed of two materials.
[0037] In an exemplary embodiment, MECT decomposes a high-energy
image and a low-energy image using a decomposition method, such as
through a CT number difference decomposition, a Compton and
photoelectric decomposition, a basis material decomposition (BMD),
or a logarithm subtraction decomposition (LSD).
[0038] The CT number difference algorithm includes calculating a
difference value in a CT or a Hounsfield number between two images
obtained at different tube potentials. In one embodiment, the
difference values are calculated on a pixel-by-pixel basis. In
another embodiment, average CT number differences are calculated
over a region of interest. The Compton and photoelectric
decomposition includes acquiring a pair of images using MECT 10,
and separately representing the attenuations from Compton and
photoelectric processes. The BMD includes acquiring two CT images,
wherein each image represents the equivalent density of one of the
basis materials. Since a material density is independent of x-ray
photon energy, these images are approximately free of
beam-hardening artifacts. Additionally, an operator can choose the
basis material to target a certain material of interest, thus
enhancing the image contrast. In use, the BMD algorithm is based on
the concept that the x-ray attenuation (in the energy region for
medical CT) of any given material can be represented by proper
density mix of other two given materials, accordingly, these two
materials are called the basis materials. In one embodiment, using
the LSD, the images are acquired with quasi-monoenergetic x-ray
spectra, and the imaged object can be characterized by an effective
attenuation coefficient for each of the two materials, therefore
the LSD does not incorporate beam-hardening corrections.
Additionally, the LSD is not calibrated, but uses a determination
of the tissue cancellation parameters, which are the ratio of the
effective attenuation coefficient of a given material at the
average energy of each exposure. In an exemplary embodiment, the
tissue cancellation parameter is primarily dependent upon the
spectra used to acquire the images, and on any additional factors
that change the measured signal intensity from that which would be
expected for a pair of ideal, mono-energetic exposures.
[0039] It should be noted that in order to optimize a multi-energy
CT system, the larger the spectra separation, the better the image
quality. Also, the photon statistics in these two energy regions
should be similar, otherwise, the poorer statistical region will
dominate the image noise.
[0040] The methods and systems described herein apply the above
principle to tissue characterization and soft-tissue volume
visualization. In specific, MECT system 10 is utilized to produce
CT images as herein described. Pre-reconstruction analysis,
post-reconstruction analysis and scout image analysis are three
techniques that can be used with MECT system 10 to provide tissue
characterization.
[0041] FIG. 3 is a flow chart representing a pre-reconstruction
analysis 54 wherein a decomposition 56 is accomplished prior to a
reconstruction 58. Computer 36 collects the acquired projection
data generated by detector array 18 (shown in FIG. 1) at discrete
angular positions of the rotating gantry 12 (shown in FIG. 1), and
passes the signals to a preprocessor 60. Preprocessor 60 re-sorts
the projection data received from computer 36 to optimize the
sequence for the subsequent mathematical processing. Preprocessor
60 also corrects the projection data from computer 36 for detector
temperature, intensity of the primary beam, gain and offset, and
other deterministic error factors. Preprocessor 60 then extracts
data corresponding to a high-energy view 62 and routes it to a
high-energy channel path 64, and routes the data corresponding to a
low-energy views 66 to a low-energy path 68. Using the high-energy
data and low-energy data, a decomposition algorithm is used to
produce two streams of projection data, which are then
reconstructed to obtain two individual images pertaining to two
different materials.
[0042] FIG. 4 is a flow chart representing a post-reconstruction
analysis wherein decomposition 56 is accomplished after
reconstruction 58. Computer 36 collects the acquired projection
data generated by detector array 18 (shown in FIG. 1) at discrete
angular positions of rotating gantry 12 (shown in FIG. 1), and
routes the data corresponding to high-energy views 62 to
high-energy path 64 and routes the data corresponding to low-energy
views 66 to low-energy path 68. A first CT image 70 corresponding
to the high-energy series of projections 62 and a second CT image
72 corresponding to low-energy series of projections 66 are
reconstructed 58. Decomposition 56 is then performed to obtain two
individual images respectively, pertaining to two different
materials. In scout image analysis, the signal flow can be similar
to FIG. 3 or FIG. 4. However, the table is moved relative to the
non-rotating gantry to acquire the data.
[0043] The use of dual energy techniques in projection x-ray
imaging may facilitate diagnosing and monitoring osteoporosis, and
determining an average fat-tissue to lean-tissue ratio (fat/lean
ratio). Dual energy techniques may also facilitate cross-sectional
or tomographic x-ray imaging for osteoporosis detection in human
subjects, and may facilitate non-destructive testing applications,
for example explosive and/or contraband detection.
[0044] The methods and systems described herein apply multi-energy
imaging to volume visualization. Techniques that allow
visualization of three-dimensional data are referred to as volume
rendering. More specifically, volume rendering is a technique used
for visualizing sampled functions of three spatial dimensions by
computing 2-D projections of a semitransparent volume. Volume
rendering is applied to medical imaging, wherein volume data is
available from X-ray CT scanners. CT scanners produce
three-dimensional stacks of parallel plane images, or slices, each
of which consist of an array of X-ray absorption coefficients.
Typical X-ray CT images have a resolution of 512.times.512.times.12
bits, and include up to 500 slices in a stack. In the
two-dimensional domain, slices can be viewed one at a time. An
advantage of CT images over conventional X-ray images is that each
slice only contains information from one plane. A conventional
X-ray image, on the other hand, contains information from all
planes, and the result is an accumulation of shadows that are a
function of the density of anything that absorbs X-rays, for
example tissue, bone, organs, etc. The availability of the stacks
of parallel data produced by CT scanners prompted the development
of techniques for viewing the data as a three-dimensional field,
rather than as individual slices. Therefore, the CT image data can
now be viewed from any viewpoint.
[0045] A number of different methods are used for viewing CT image
data as a three-dimensional field, for example, including rendering
voxels in a binary partitioned space, marching cubes, and ray
casting. When rendering voxels in a binary partitioned space,
choices are made for the entire voxel. This may produce a "blocky"
image. In addition, rendering voxels in a binary partitioned space
may result in a lack of dynamic range in the computed surface
normals, which will produce images with relatively poor
shading.
[0046] Using marching cubes for viewing CT image data in a
three-dimensional field solves some of the problems associated with
rendering voxels in a binary partitioned space. However, using
marching cubes requires that a binary decision be made as to the
position of the intermediate surface that is extracted and
rendered. Furthermore, extracting an intermediate structure may
cause false positives (artifacts that do not exist) and false
negatives (discarding small or poorly defined features).
[0047] Using ray casting for viewing CT image data in a
three-dimensional field facilitates use of the three-dimensional
data without attempting to impose any geometric structure on it.
Ray casting solves one of the most important limitations of surface
extraction techniques, namely the way in which surface extraction
techniques display a projection of a thin shell in the acquisition
space. More specifically, surface extraction techniques fail to
take into account that, particularly in medical imaging, data may
originate from fluid and other materials, which may be partially
transparent and should be modeled as such. Ray casting, however,
does take into account that data may originate from fluid and other
materials, and can model materials that are partially
transparent.
[0048] FIG. 5 is a schematic illustration of a method 80 for
soft-tissue volume visualization using MECT system 10 (shown in
FIGS. 1 and 2). Method 80 describes 3D visualization using a
combination of physics-based segmentation (multi-energy
decomposition data) and image-based segmentation. More
specifically, method 80 includes acquiring 82 MECT anatomic image
data for an object (not shown), wherein the anatomic image data
includes a high-energy image (H) and a low-energy image (L). The
anatomic image data is then decomposed 84 to obtain a density image
representing soft-tissue within the object and a density image
representing bone material within the object. The high-energy
image, low-energy image, soft-tissue density image, and
bone-material density image are then segmented 86 using image-based
segmentation to determine a region of interest within the object.
In one embodiment, high-energy image, low-energy image, the
soft-tissue density image, and the bone-material density image are
segmented 86 individually using image-based segmentation. In
another embodiment, high-energy image, low-energy image, the
soft-tissue density image, and the bone-material density image are
segmented 86 in combination using image-based segmentation.
[0049] Several segmentation techniques can be used for image-based
segmentation to determine a region of interest within the object,
including, but not limited to, Hounsfield or CT number (threshold)
techniques, iterative thresholding, k-means segmentation, edge
detection, edge linking, curve fitting, curve smoothing, 2D/3D
morphological filtering, region growing, fuzzy clustering,
image/volume measurements, heuristics, knowledge-based rules,
decision trees, and neural networks. Segmentation of a region of
interest can be performed manually and/or automatically. In one
embodiment, the high-energy image, the low-energy image, the
soft-tissue density image, and the bone-material density image are
segmented manually to determine a region of interest within the
object by displaying the data and a user delineating the region of
interest using a mouse or any other suitable interface, for
example, a touch screen, eye-tracking, and/or voice commands. In
addition, in one embodiment, the high-energy image, the low-energy
image, the soft-tissue density image, and the bone-material density
image are automatically segmented to determine a region of interest
with the object by using an algorithm that utilizes prior
knowledge, such as the shape and size of a mass, to automatically
delineate the area of interest. In yet another embodiment, the
high-energy image, the low-energy image, the soft-tissue density
image, and the bone-material density image are segmented to
determine a region of interest within the object semi-automatically
by combining manual and automatic segmentation.
[0050] The image-based segmented high-energy anatomical image data,
the image-based segmented soft-tissue density image, and the
image-based segmented bone density image are then used 88 to obtain
a soft-tissue image including bone material for the region of
interest within the object. The soft-tissue image including bone
material is then used to build a three-dimensional image, which in
turn is used for rendering 90 to provide high-contrast rendered
images. In an alternative embodiment, the high-energy image, the
low-energy image, the soft-tissue density image, and the
bone-material density image are not segmented 86, but rather, at
least one of the high-energy image, the low-energy image, the
soft-tissue density image, and the bone-material density image are
used to build a three-dimensional image, which is used for
rendering 90 to provide high-contrast rendered images. Rendering 90
is performed using conventional rendering techniques, such as, for
example, techniques describe in The Visualization Toolkit, An
Object-Orientated Approach to 3D Graphics, Will Shroeder, Ken
Martin, and Bill Lorensen, Prentice-Hall 1996. In one embodiment,
volume rendering is used to provide high-contrast rendered images.
In another embodiment, surface rendering is used to provide
high-contrast rendered images.
[0051] FIG. 6 is a schematic illustration of a method 100 for
soft-tissue volume visualization using MECT system 10 (shown in
FIGS. 1 and 2). More specifically, method 100 is a specific example
of one embodiment of method 80. In use, method 100 includes
acquiring 102 MECT anatomic image data for a region of interest
within an object (not shown) or, alternatively, the object in its
entirety, wherein the anatomic image data includes a high-energy
image and a low-energy image. The anatomic image data is then
decomposed 104 to obtain a density image representing soft-tissue
within the region of interest (I.sub.s) and a density image
representing bone-material within the region of interest (I.sub.b).
In one embodiment, the density image representing soft-tissue is
obtained using the following decomposition equation: 1 I s = H L w
s ,
[0052] where 0<w.sub.s<w.sub.b<1. Additionally, and in one
embodiment, the density image representing bone-material is
obtained using the following decomposition equation: 2 I b = H L wb
,
[0053] where 0<w.sub.s<w.sub.b<1.
[0054] The density image representing soft-tissue is then contrast
matched 106 with a standard CT image of the region of interest. For
example, in the exemplary embodiment, the contrast of structures
within the soft-tissue density image are matched with the
corresponding structures in the high-energy anatomical image data
H. In one embodiment, the soft-tissue density image is contrast
matched 106 with the high-energy anatomical image data H by solving
the above decomposition equations for H in terms of I.sub.s,
J.sub.b, w.sub.b, and w.sub.s, to obtain the following
relationship: 3 H = I s w b w b - w s I b - w s w b - w s .
[0055] By differentiation of the logarithm of the above equation,
the following contrast equation is derived: 4 C ( H ) = w b w b - w
s C ( I s ) - w s w b - w s C ( I b ) ,
[0056] wherein C(.) represents the contrast in the image. From the
above C(H) equation, it may be evident that while matching the
contrast in the soft-tissue density image and the corresponding
structures in the high-energy anatomical image data, the contrast
[C(I.sub.b)] resulting from the bone-material density image may
need to be suppressed. In one embodiment, to reduce the fine-detail
contrast while preserving the scaling, the bone-material density
image is low-pass filtered such that all structural information is
eliminated. Accordingly, a contrast matched soft-tissue image
(I.sub.HS) is obtained from the following equation: 5 I HS = I s w
b w b - w s LPF ( I b - w s w b - w s ) ,
[0057] wherein the function LPF(.) performs the low-pass filtering
of the bone-material density image. In one embodiment, a boxcar
filter is used as LPF(.) to perform low-pass filtering of the
image, wherein the boxcar filter smoothes an image by the average
of a given neighborhood. Using boxcar filtering, each point in an
image requires only four arithmetic operations, irrespective of
kernel size. In addition, and in one embodiment, the length of the
separable kernel is variable. In an alternative embodiment, a bone
mask is derived by segmenting the bone image. The bone mask is
inverted to obtain the soft-tissue mask. The inverted bone mask is
superimposed on the soft-tissue image and the soft-tissue regions
are contrast-matched to the soft-tissue regions of the standard
image. Special care is taken at the borders of the mask to
alleviate problems resulting from the bone-soft-tissue transition
region. In one embodiment, the border regions can be rank-order
filtered, for example, using median criterion to suppress high
intensity transition rings in 3D. The resulting image is a
contrast-matched soft-tissue image.
[0058] The bone-material density image is then thresholded 108 to
produce a first binary mask image containing bone and
calcification. More specifically, because the bone-material density
image includes both calcium and bone, the bone-material density
image is thresholded 108 to separate out high-contrast bone regions
and the high-contrast calcification regions from the low-contrast
regions. Islands smaller than a pre-specified size are then
extracted 110 from the first binary mask image to produce a second
binary mask image corresponding substantially to calcification. In
one embodiment, an algorithm using the connectivity of binary
pixels is used to extract 110 small islands from the first binary
mask image to produce the second binary mask image. For example, in
one embodiment four-connectivity is used to determine the size of
connected components and extract 110 islands smaller than the
pre-specified limit to produce the second binary mask image. In
another embodiment, eight-connectivity is used to determine the
size of connected components and extract 110 islands smaller than
the pre-specified limit to produce the second binary mask
image.
[0059] The original pixel values from the high-energy anatomical
image data that correspond to the second binary mask image are then
merged 112 with the contrast-matched soft-tissue image to obtain a
soft-tissue image including calcification. More specifically, the
regions within the high-energy anatomical image data that
correspond to the second binary mask image are extracted from the
high-energy anatomical image and merged with the contrast-matched
soft-tissue image to produce a soft-tissue image including
calcification. The soft-tissue image including calcification is
then used to build a three-dimensional image, which in turn is used
for rendering 114 to provide high-contrast rendered images.
Rendering 114 is performed using conventional rendering techniques,
such as, for example, techniques describe in The Visualization
Toolkit, An Object-Orientated Approach to 3D Graphics, Will
Shroeder, Ken Martin, and Bill Lorensen, Prentice-Hall 1996. In one
embodiment, volume rendering is used to provide high-contrast
rendered images. In another embodiment, surface rendering is used
to provide high-contrast rendered images. In an alternative
embodiment wherein calcification identification is not desired for
visualization, normalized soft-tissue image data is used to produce
three-dimensional renderings of soft-tissue.
[0060] FIG. 7 is a schematic illustration of a known surgical
navigation system 130. System 130 includes a surgical patient 132,
image data 134 for patient 132, a reference means 136 having a
reference point on a reference coordinate system that is external
to patient 132, a position and orientation determination means 138
coupled to patient 132 for determining the position and orientation
of patient 132, a surgical instrument 140, a surgical instrument
position determination means 142 coupled to instrument 140 for
determining the position of surgical instrument 140, and a display
144 coupled to a computer 146. Computer 146 converts patient
display data to objective display data, converts instrument
location and orientation data for display on display 144, and
provides a known relationship between patient 132 and the reference
point. Computer 146 displays patient image data 134 and instrument
140 on display 144 substantially simultaneously.
[0061] FIG. 8 is a schematic illustration of a surgical navigation
system 150 for use with method 80 (shown in FIG. 5) to provide
surgical instrument mapping for two volumes simultaneously and
assist in identification of subtle soft-tissue structures and their
spatial relationship to bone. System 150 includes a surgical
patient 152, image data 154 for patient 152 including multi-energy
CT data, a reference means 156 having a reference point on a
reference coordinate system that is external to patient 152, a
position and orientation determination means 158 coupled to patient
152 for determining the position and orientation of patient 152, a
surgical instrument 160, a surgical instrument position
determination means 162 coupled to instrument 160 for determining
the position of surgical instrument 160, and a display 164 coupled
to a computer 166. Computer 166 converts patient display data to
objective display data, converts instrument location and
orientation data for display on display 164, and provides a known
relationship between patient 152 and the reference point. Computer
166 displays patient image data 154 and instrument 160 on display
164 substantially simultaneously. In addition, computer 166
displays a standard image of patient image data 154 on display 164,
displays a soft-tissue only image of patient image data 154 on
display 164, and displays a bone-only image of patient image data
154 on display 164. In one embodiment, computer 166 displays the
standard image, the soft-tissue only image, and the bone-only image
substantially simultaneously. In another embodiment, computer 166
includes a toggling capability for toggling between display of the
standard image, the soft-tissue only image, and the bone-only image
on display 164.
[0062] FIG. 9 is a schematic illustration of a known radiation
system 180. System 180 includes a radiation therapy patient 182,
image data 184 for patient 182, a reference means 186 having a
reference point on a reference coordinate system that is external
to patient 182, a position and orientation determination means 188
coupled to patient 182 for determining the position and orientation
of patient 182, a radiation therapy sub-system 190, a simulation
and modeling means 192 for planning paths and dosage, and a display
194 coupled to a computer 196. Computer 146 converts patient
display data to objective display data, converts radiation
localization for display on display 194, and provides a known
relationship between patient 182 and the reference point. Computer
196 displays patient image data 184 and the radiation localization
on display 194 substantially simultaneously.
[0063] FIG. 10 is a schematic illustration of a radiation system
210 for use with method 80 (shown in FIG. 5) to provide radiation
therapy planning and simulation calculations. System 210 includes a
radiation therapy patient 212, image data 214 for patient 212
including multi-energy CT image data, a reference means 216 having
a reference point on a reference coordinate system that is external
to patient 212, a position and orientation determination means 218
coupled to patient 212 for determining the position and orientation
of patient 212, a radiation therapy sub-system 220, a simulation
and modeling means 222 for planning paths and dosage, and a display
224 coupled to a computer 226. Computer 226 converts patient
display data to objective display data, converts radiation
localization for display on display 224, and provides a known
relationship between patient 212 and the reference point. Computer
226 displays patient image data 184 and the radiation localization
on display 194 substantially simultaneously. In addition, computer
226 displays a standard image of patient image data 214 on display
224, displays a soft-tissue only image of patient image data 214 on
display 224, and displays a bone-only image of patient image data
214 on display 224. In one embodiment, computer 226 displays the
standard image, the soft-tissue only image, and the bone-only image
substantially simultaneously. In another embodiment, computer 226
includes a toggling capability for toggling between display of the
standard image, the soft-tissue only image, and the bone-only image
on display 224.
[0064] The above-described methods and systems facilitate
augmenting segmentation capabilities of multi-energy imaging with a
method for image-based segmentation, and may facilitate real-time
volume buildup and visualization of soft-tissue. More specifically,
the above-described methods and systems facilitate segmenting bone
material from an image while retaining calcification within the
image, facilitate providing traditional surgical instrument mapping
for two volumes simultaneously, facilitate identification of subtle
soft-tissue structures and their spatial relationship to bone,
facilitate computer simulation of dosage and paths for radiation
therapy, and facilitate improving radiation therapy planning and
simulation calculations.
[0065] Exemplary embodiments of MECT methods and systems are
described above in detail. The methods and systems are not limited
to the specific embodiments described herein, but rather,
components of each method and system may be utilized independently
and separately from other components described herein. In addition,
each method and system component can also be used in combination
with other components described herein.
[0066] While the invention has been described in terms of various
specific embodiments, those skilled in the art will recognize that
the invention can be practiced with modification within the spirit
and scope of the claims.
* * * * *